Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and dee...
Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning
About this item
Full title
Author / Creator
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
Formats
Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Subjects
More information
Scope and Contents
Contents
Purpose
Microvascular invasion (MVI) is a valuable predictor of survival in hepatocellular carcinoma (HCC) patients. This study developed predictive models using eXtreme Gradient Boosting (XGBoost) and deep learning based on CT images to predict MVI preoperatively.
Methods
In total, 405 patients were included. A total of 7302 radiomic feat...
Alternative Titles
Full title
Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7873117
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7873117
Other Identifiers
ISSN
0171-5216
E-ISSN
1432-1335
DOI
10.1007/s00432-020-03366-9
